Testing the Real Capacity of the Battery

Csikós, Flórián; Csikós, Fanni · 2025 · Crossref

DOI: 10.64808/engineeringperspective.1791078

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Summary

This study addresses the critical need for accurate, non-invasive diagnostics of lithium-ion traction batteries in electric vehicles (EVs). As EV adoption grows, monitoring battery State of Health (SOH) and State of Charge (SOC) is essential for safety, longevity, and economic viability. Traditional methods often require disassembling the battery pack, which is complex, time-consuming, and safety-critical. The authors aim to develop a contactless diagnostic method using the vehicle’s On-Board Diagnostics (OBD) interface and Controller Area Network (CAN) bus to retrieve real-time battery parameters without physical intrusion. This approach supports sustainable vehicle usage by enabling targeted module replacement and reducing premature battery disposal. The research focuses on a 2020 Volkswagen e-Golf equipped with a 35.8 kWh lithium-ion battery pack consisting of 88 series-connected cells. The experimental design involved two measurement scenarios: controlled discharge tests on a chassis dynamometer and reference measurements under real-world driving conditions. Data acquisition was performed using a Kvaser Memorator R SemiPro connected to the CAN bus, with commands sent via a MATLAB Simulink model. The authors utilized a Database (DBC) file to decode multiplexed CAN messages, extracting individual cell voltages, temperatures, and pack-level metrics. These data were validated against parallel diagnostic read-outs using VCDS software. The dynamometer tests employed alternating high- and low-load segments at constant speed with regenerative braking disabled to ensure repeatable, undistorted discharge curves. The results demonstrate that contactless CAN-based data acquisition effectively detects cell imbalances and early signs of degradation. The calculated SOH for the test vehicle was 99.98%, reflecting its low mileage and optimal condition. Analysis of individual cell voltages revealed that while overall voltage differences were minimal, specific cells exhibited distinct behaviors under load. For instance, Cell 21 showed lower discharge voltages and greater fluctuations compared to Cell 32, indicating higher internal resistance and potential thermal stress. The study found that monitoring voltages during dynamic load provides clearer indications of faulty cells than static fully charged or discharged states, as the Battery Management System (BMS) equalizes voltages at these extremes. Cells located in the center of modules, exposed to higher heat, were identified as likely candidates for earlier failure due to increased internal resistance. The significance of this work lies in validating a scalable, reproducible method for battery health assessment that does not require pack disassembly. The findings confirm that chassis dynamometer testing offers superior data clarity and repeatability compared to on-road testing, primarily due to controlled thermal environments and the absence of regenerative braking transients. This methodology supports the development of efficient diagnostic tools for both research and industrial applications, facilitating predictive maintenance and second-life battery decisions. The authors conclude that extending this approach to higher-mileage vehicles and dynamic load profiles will further enhance the reliability of battery monitoring systems, contributing to the broader goals of EV sustainability and economic efficiency.

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• dms validation